Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f5ceeb2a3c8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f5ceea50f60>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [35]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], name='input_real')
    input_z = tf.placeholder(tf.float32, [None, z_dim], name='z_dim')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')

    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [106]:
def activate_fn(input):
    alpha = 0.2
    # swish
    return input * tf.nn.sigmoid(input)
#     return tf.maximum(alpha * input, input)

Xavier = tf.contrib.layers.xavier_initializer()
keep_prob = 0.2
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        # 28x28x3 -> 14x14x64
        L1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', kernel_initializer=Xavier)
        L1 = tf.layers.dropout(L1, keep_prob, training=True)
        L1 = activate_fn(L1)
        
        # 7x7x128
        L2 = tf.layers.conv2d(L1, 128, 5, strides=2, padding='same', kernel_initializer=Xavier)
        L2 = tf.layers.dropout(L2, keep_prob, training=True)
        L2 = tf.layers.batch_normalization(L2, training=True)
        L2 = activate_fn(L2)
        
        # 4x4x256
        L3 = tf.layers.conv2d(L2, 256, 5, strides=2, padding='same', kernel_initializer=Xavier)
        L3 = tf.layers.dropout(L3, keep_prob, training=True)
        L3 = tf.layers.batch_normalization(L3, training=True)
        L3 = activate_fn(L3)
#         print('DL3', L3.shape)
        # Flatten
        flat = tf.reshape(L3, [-1, 4*4*256])
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
    return  out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [107]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope('generator', reuse=not is_train):
        
        
        # Fully connect
        L1 = tf.layers.dense(z, 4*4*256)
        
        # 4x4x256
        L2 = tf.reshape(L1, (-1, 4, 4, 256))
        L2 = tf.layers.dropout(L2, keep_prob, training=is_train)
        L2 = tf.layers.batch_normalization(L2, training=is_train)
        L2 = activate_fn(L2)
#         print('GL2', L2.shape)
        # 7x7x128 因为7x7经过4x4的filter,1x1的strides和Valid之后会变成4x4,所以反过来就是7x7
        L3 = tf.layers.conv2d_transpose(L2, 128, 4, strides=1, padding='valid', kernel_initializer=Xavier)
        L3 = tf.layers.dropout(L3, keep_prob, training=is_train)
        L3 = tf.layers.batch_normalization(L3, training=is_train)
        L3 = activate_fn(L3)
#         print('GL3', L3.shape)
        
        # 14x14x64
        L4 = tf.layers.conv2d_transpose(L3, 64, 5, strides=2, padding='same', kernel_initializer=Xavier)
        L4 = tf.layers.dropout(L4, keep_prob, training=is_train)
        L4 = tf.layers.batch_normalization(L4, training=is_train)
        L4 = activate_fn(L4)
#         print('GL4', L4.shape)
        
        # Output 28x28xout_channel_dim
        logits = tf.layers.conv2d_transpose(L4, out_channel_dim, 5, strides=2, padding='same', kernel_initializer=Xavier)
        out = tf.tanh(logits)
        
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [108]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, True)
    d_model_real, d_logits_real = discriminator(input_real, False)
    d_model_fake, d_logits_fake = discriminator(g_model, True)
    smooth = 0.1
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*(1-smooth)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [109]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
        
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [110]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [111]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    out_channel_dim = 3 if data_image_mode == 'RGB' else 1
    d_loss, g_loss = model_loss(input_real, input_z, out_channel_dim)
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    steps = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images *= 2
                # Input for Generator
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                if steps % 100 == 0:
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    print("Epoch {}/{} - Steps {}...".format(epoch_i+1, epoch_count, steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    show_generator_output(sess, 16, input_z, out_channel_dim, data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [112]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2 - Steps 100... Discriminator Loss: 1.2887... Generator Loss: 0.8297
Epoch 1/2 - Steps 200... Discriminator Loss: 1.2400... Generator Loss: 1.0311
Epoch 1/2 - Steps 300... Discriminator Loss: 1.4126... Generator Loss: 0.8588
Epoch 1/2 - Steps 400... Discriminator Loss: 1.2536... Generator Loss: 1.1806
Epoch 1/2 - Steps 500... Discriminator Loss: 1.2047... Generator Loss: 1.2151
Epoch 1/2 - Steps 600... Discriminator Loss: 1.1225... Generator Loss: 0.9966
Epoch 1/2 - Steps 700... Discriminator Loss: 1.2530... Generator Loss: 0.8194
Epoch 1/2 - Steps 800... Discriminator Loss: 1.2663... Generator Loss: 0.8995
Epoch 1/2 - Steps 900... Discriminator Loss: 1.2353... Generator Loss: 1.0702
Epoch 1/2 - Steps 1000... Discriminator Loss: 1.2375... Generator Loss: 1.0353
Epoch 1/2 - Steps 1100... Discriminator Loss: 1.4049... Generator Loss: 0.7348
Epoch 1/2 - Steps 1200... Discriminator Loss: 1.3386... Generator Loss: 0.9838
Epoch 1/2 - Steps 1300... Discriminator Loss: 1.1793... Generator Loss: 0.7751
Epoch 1/2 - Steps 1400... Discriminator Loss: 1.2894... Generator Loss: 0.9780
Epoch 1/2 - Steps 1500... Discriminator Loss: 1.1738... Generator Loss: 0.8695
Epoch 1/2 - Steps 1600... Discriminator Loss: 1.2264... Generator Loss: 0.9486
Epoch 1/2 - Steps 1700... Discriminator Loss: 1.3131... Generator Loss: 0.8409
Epoch 1/2 - Steps 1800... Discriminator Loss: 1.2323... Generator Loss: 0.9238
Epoch 2/2 - Steps 1900... Discriminator Loss: 1.3460... Generator Loss: 0.9150
Epoch 2/2 - Steps 2000... Discriminator Loss: 1.1652... Generator Loss: 0.7912
Epoch 2/2 - Steps 2100... Discriminator Loss: 1.2018... Generator Loss: 1.2062
Epoch 2/2 - Steps 2200... Discriminator Loss: 1.3769... Generator Loss: 0.8319
Epoch 2/2 - Steps 2300... Discriminator Loss: 1.1163... Generator Loss: 1.0300
Epoch 2/2 - Steps 2400... Discriminator Loss: 1.0884... Generator Loss: 0.8211
Epoch 2/2 - Steps 2500... Discriminator Loss: 1.2004... Generator Loss: 1.0217
Epoch 2/2 - Steps 2600... Discriminator Loss: 1.2785... Generator Loss: 1.0094
Epoch 2/2 - Steps 2700... Discriminator Loss: 1.2266... Generator Loss: 1.0367
Epoch 2/2 - Steps 2800... Discriminator Loss: 1.0692... Generator Loss: 1.1899
Epoch 2/2 - Steps 2900... Discriminator Loss: 1.1386... Generator Loss: 0.8778
Epoch 2/2 - Steps 3000... Discriminator Loss: 1.2984... Generator Loss: 1.0128
Epoch 2/2 - Steps 3100... Discriminator Loss: 1.1037... Generator Loss: 1.1557
Epoch 2/2 - Steps 3200... Discriminator Loss: 1.1825... Generator Loss: 1.0874
Epoch 2/2 - Steps 3300... Discriminator Loss: 1.0719... Generator Loss: 1.0914
Epoch 2/2 - Steps 3400... Discriminator Loss: 1.0998... Generator Loss: 1.1375
Epoch 2/2 - Steps 3500... Discriminator Loss: 1.0388... Generator Loss: 1.1516
Epoch 2/2 - Steps 3600... Discriminator Loss: 1.2142... Generator Loss: 0.8093
Epoch 2/2 - Steps 3700... Discriminator Loss: 1.1721... Generator Loss: 1.4185

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [113]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1 - Steps 100... Discriminator Loss: 0.9115... Generator Loss: 1.9563
Epoch 1/1 - Steps 200... Discriminator Loss: 0.4699... Generator Loss: 2.7992
Epoch 1/1 - Steps 300... Discriminator Loss: 1.2119... Generator Loss: 0.9110
Epoch 1/1 - Steps 400... Discriminator Loss: 1.1806... Generator Loss: 1.2710
Epoch 1/1 - Steps 500... Discriminator Loss: 1.4766... Generator Loss: 0.9352
Epoch 1/1 - Steps 600... Discriminator Loss: 1.3984... Generator Loss: 0.9560
Epoch 1/1 - Steps 700... Discriminator Loss: 1.2846... Generator Loss: 1.1347
Epoch 1/1 - Steps 800... Discriminator Loss: 1.2784... Generator Loss: 0.9286
Epoch 1/1 - Steps 900... Discriminator Loss: 1.3078... Generator Loss: 1.0264
Epoch 1/1 - Steps 1000... Discriminator Loss: 1.2613... Generator Loss: 0.8251
Epoch 1/1 - Steps 1100... Discriminator Loss: 1.4188... Generator Loss: 0.9319
Epoch 1/1 - Steps 1200... Discriminator Loss: 1.2725... Generator Loss: 0.8400
Epoch 1/1 - Steps 1300... Discriminator Loss: 1.2760... Generator Loss: 1.0455
Epoch 1/1 - Steps 1400... Discriminator Loss: 1.4021... Generator Loss: 0.8133
Epoch 1/1 - Steps 1500... Discriminator Loss: 1.3555... Generator Loss: 0.8801
Epoch 1/1 - Steps 1600... Discriminator Loss: 1.4792... Generator Loss: 0.8273
Epoch 1/1 - Steps 1700... Discriminator Loss: 1.3314... Generator Loss: 0.8966
Epoch 1/1 - Steps 1800... Discriminator Loss: 1.2335... Generator Loss: 0.8632
Epoch 1/1 - Steps 1900... Discriminator Loss: 1.4182... Generator Loss: 0.8387
Epoch 1/1 - Steps 2000... Discriminator Loss: 1.3012... Generator Loss: 0.8490
Epoch 1/1 - Steps 2100... Discriminator Loss: 1.4668... Generator Loss: 0.7193
Epoch 1/1 - Steps 2200... Discriminator Loss: 1.2779... Generator Loss: 0.9124
Epoch 1/1 - Steps 2300... Discriminator Loss: 1.3217... Generator Loss: 0.8145
Epoch 1/1 - Steps 2400... Discriminator Loss: 1.2262... Generator Loss: 0.8512
Epoch 1/1 - Steps 2500... Discriminator Loss: 1.2946... Generator Loss: 0.7795
Epoch 1/1 - Steps 2600... Discriminator Loss: 1.3856... Generator Loss: 0.7827
Epoch 1/1 - Steps 2700... Discriminator Loss: 1.3406... Generator Loss: 0.7781
Epoch 1/1 - Steps 2800... Discriminator Loss: 1.2715... Generator Loss: 0.9205
Epoch 1/1 - Steps 2900... Discriminator Loss: 1.3613... Generator Loss: 0.6933
Epoch 1/1 - Steps 3000... Discriminator Loss: 1.4217... Generator Loss: 0.7782
Epoch 1/1 - Steps 3100... Discriminator Loss: 1.3533... Generator Loss: 0.7669
Epoch 1/1 - Steps 3200... Discriminator Loss: 1.3711... Generator Loss: 0.8086
Epoch 1/1 - Steps 3300... Discriminator Loss: 1.4348... Generator Loss: 0.8072
Epoch 1/1 - Steps 3400... Discriminator Loss: 1.3715... Generator Loss: 0.7225
Epoch 1/1 - Steps 3500... Discriminator Loss: 1.3380... Generator Loss: 0.8024
Epoch 1/1 - Steps 3600... Discriminator Loss: 1.3514... Generator Loss: 0.7700
Epoch 1/1 - Steps 3700... Discriminator Loss: 1.4249... Generator Loss: 0.7727
Epoch 1/1 - Steps 3800... Discriminator Loss: 1.3197... Generator Loss: 0.8592
Epoch 1/1 - Steps 3900... Discriminator Loss: 1.4040... Generator Loss: 0.7282
Epoch 1/1 - Steps 4000... Discriminator Loss: 1.4135... Generator Loss: 0.7534
Epoch 1/1 - Steps 4100... Discriminator Loss: 1.3569... Generator Loss: 0.8054
Epoch 1/1 - Steps 4200... Discriminator Loss: 1.3105... Generator Loss: 0.8933
Epoch 1/1 - Steps 4300... Discriminator Loss: 1.2593... Generator Loss: 0.8649
Epoch 1/1 - Steps 4400... Discriminator Loss: 1.2761... Generator Loss: 0.8827
Epoch 1/1 - Steps 4500... Discriminator Loss: 1.3832... Generator Loss: 0.7385
Epoch 1/1 - Steps 4600... Discriminator Loss: 1.3207... Generator Loss: 0.8363
Epoch 1/1 - Steps 4700... Discriminator Loss: 1.2701... Generator Loss: 0.7715
Epoch 1/1 - Steps 4800... Discriminator Loss: 1.3315... Generator Loss: 0.7813
Epoch 1/1 - Steps 4900... Discriminator Loss: 1.2091... Generator Loss: 0.8613
Epoch 1/1 - Steps 5000... Discriminator Loss: 1.2987... Generator Loss: 0.8856
Epoch 1/1 - Steps 5100... Discriminator Loss: 1.3335... Generator Loss: 0.8970
Epoch 1/1 - Steps 5200... Discriminator Loss: 1.2534... Generator Loss: 0.9212
Epoch 1/1 - Steps 5300... Discriminator Loss: 1.3448... Generator Loss: 0.8839
Epoch 1/1 - Steps 5400... Discriminator Loss: 1.3552... Generator Loss: 0.7192
Epoch 1/1 - Steps 5500... Discriminator Loss: 1.2658... Generator Loss: 0.8398
Epoch 1/1 - Steps 5600... Discriminator Loss: 1.3533... Generator Loss: 0.9344
Epoch 1/1 - Steps 5700... Discriminator Loss: 1.3097... Generator Loss: 0.9262
Epoch 1/1 - Steps 5800... Discriminator Loss: 1.3491... Generator Loss: 0.8723
Epoch 1/1 - Steps 5900... Discriminator Loss: 1.3905... Generator Loss: 0.8014
Epoch 1/1 - Steps 6000... Discriminator Loss: 1.2490... Generator Loss: 0.8491
Epoch 1/1 - Steps 6100... Discriminator Loss: 1.3015... Generator Loss: 0.8930
Epoch 1/1 - Steps 6200... Discriminator Loss: 1.1350... Generator Loss: 0.8565
Epoch 1/1 - Steps 6300... Discriminator Loss: 1.2654... Generator Loss: 1.0172

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.